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Computational Modeling of Signaling Networks

  • Book
  • © 2023

Overview

Editors:
  1. Lan K. Nguyen

    1. Biomedicine Discovery Institute, Monash University, Clayton, Australia

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  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2634)

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About this book

This volume focuses on the computational modeling of cell signaling networks and the application of these models and model-based analysis to systems and personalized medicine. Chapters guide readers through various modeling approaches for signaling networks, new methods and techniques that facilitate model development and analysis, and new applications of signaling network modeling towards systems and personalized treatment of cancer. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.

 

Authoritative and cutting-edge, Computational Modeling of Signaling Networks aims to benefit a wide spectrum of readers including researchers from the biological as well as computational systems biology communities.

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Table of contents (17 protocols)

  1. Front Matter

    Pages i-xi
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  2. Advances in Computational Modelling of Signalling Networks

    1. Front Matter

      Pages 1-1
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    2. Design Principles Underlying Robust Adaptation of Complex Biochemical Networks

      • Robyn P. Araujo, Lance A. Liotta
      Pages 3-32

    3. Multi-Dimensional Analysis of Biochemical Network Dynamics Using pyDYVIPAC

      • Yunduo Lan, Lan K. Nguyen
      Pages 33-58

    4. A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction

      • Fabian Fröhlich
      Pages 59-86

    5. Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks

      • Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu
      Pages 87-105

    6. A Practical Guide to Reproducible Modeling for Biochemical Networks

      • Veronica L. Porubsky, Herbert M. Sauro
      Pages 107-138

    7. Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

      • Krishna Kumar, Debaleena Bhowmik, Sapan Mandloi, Anupam Gautam, Abhishake Lahiri, Nupur Biswas et al.
      Pages 139-151

    8. Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations

      • Tagari Samanta, Sandip Kar
      Pages 153-165

    9. Meta-Dynamic Network Modelling for Biochemical Networks

      • Anthony Hart, Lan K. Nguyen
      Pages 167-189

    10. Rapid Particle-Based Simulations of Cellular Signalling with the FLAME-Accelerated Signalling Tool (FaST) and GPUs

      • Gavin Fullstone
      Pages 191-212

  3. Advances in Integrative Analysis of Signalling Networks

    1. Front Matter

      Pages 213-213
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    2. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

      • Uddipan Sarma, Lorenz Ripka, Uchenna Alex Anyaegbunam, Stefan Legewie
      Pages 215-251

    3. Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications

      • Johannes Nicolaus Wibisana, Takehiko Inaba, Yasushi Sako, Mariko Okada
      Pages 253-266

    4. Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single Cell Data

      • Fabian Konrath, Alexander Loewer, Jana Wolf
      Pages 267-284

    5. Live-Cell Sender-Receiver Co-cultures for Quantitative Measurement of Paracrine Signaling Dynamics, Gene Expression, and Drug Response

      • Michael Pargett, Abhineet R. Ram, Vaibhav Murthy, Alexander E. Davies
      Pages 285-314

    6. Application of Optogenetics to Probe the Signaling Dynamics of Cell Fate Decision-Making

      • Heath E. Johnson
      Pages 315-326

  4. Application of Integrative Modelling and Analysis of Signalling Networks in Diseases

    1. Front Matter

      Pages 327-327
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    2. Computational Random Mutagenesis to Investigate RAS Mutant Signaling

      • Edward C. Stites
      Pages 329-335

    3. Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

      • Wei He, Ayesha N. Shajahan-Haq, William T. Baumann
      Pages 337-355
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Editors and Affiliations

  • Biomedicine Discovery Institute, Monash University, Clayton, Australia

    Lan K. Nguyen

Bibliographic Information

  • Book Title: Computational Modeling of Signaling Networks

  • Editors: Lan K. Nguyen

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-3008-2

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2023

  • Hardcover ISBN: 978-1-0716-3007-5Published: 20 April 2023

  • Softcover ISBN: 978-1-0716-3010-5Published: 20 April 2023

  • eBook ISBN: 978-1-0716-3008-2Published: 19 April 2023

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 386

  • Number of Illustrations: 7 b/w illustrations, 118 illustrations in colour

  • Topics: Bioinformatics, Cancer Research, Cell Biology

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